A TPC Benchmark of Hive LLAP and Comparison with Presto
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It is a TPC/H/DS benchmark on both Hive (Low Latency Analytical Processing) and Presto, comparing the two popular bigdata query engines. The results shows significant advantages of Hive LLAP on performance and durability.
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Raft protocol has been successfully used for consistent metadata replication; however, using it for data replication poses unique challenges. Apache Ratis is a RAFT implementation targeted at high throughput data replication problems. Apache Ratis is being successfully used as a consensus protocol for data stored in Ozone (object store) and Quadra(block device) to provide data throughput that saturates the network links and disk bandwidths.
Pluggable nature of Ratis renders it useful for multiple use cases including high availability, data or metadata replication, and ensuring consistency semantics.
This talk presents the design challenges to achieve high throughput and how Apache Ratis addresses them. We talk about specific optimizations that have been implemented to minimize overheads and scale up the throughput while maintaining correctness of the consistency protocol. The talk also explains how systems like Ozone take advantage of Ratis’s implementation choices to achieve scale. We will discuss the current performance numbers and also future optimizations. MUKUL KUMAR SINGH, Staff Software Engineer, Hortonworks and LOKESH JAIN, Software Engineer, Hortonworks
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Presenter:
Chaitanya Chebolu, Committer for Apache Apex and Software Engineer at DataTorrent.
In this session we will cover the use-case of ingesting data from Kafka and writing to HDFS with a couple of processing operators - Parser, Dedup, Transform.
Comparative Performance Analysis of AWS EC2 Instance Types Commonly Used for ...
Many organizations today have already migrated Hadoop workloads to cloud infrastructure or they are actively planning to do such a migration. A common question in this scenario is "Which instance types should I use for my Hadoop cluster?" There are nuances to cloud infrastructure that require careful consideration when deciding which instances types to use. This session will show the results of performance comparison of Amazon Web Services (AWS) Elastic Compute Cloud (EC2) instance types commonly used in Hadoop clusters. More importantly, we will discuss the relative cost comparison of these instance types to demonstrate the which AWS instances offer the best price to performance ratio using standard benchmarks. Attendees of this session with leave with a better understanding of the performance of AWS EC2 instance types when used for Hadoop workloads and be able to make more informed decisions about which instance types makes the most sense for their needs.
Speakers
Michael Young, Senior Solutions Engineer, Hortonworks
Marcus Waineo, Principal Solutions Engineer, Hortonworks
Hive & HBase for Transaction Processing Hadoop Summit EU Apr 2015
The document discusses using Hive, HBase, Phoenix, and Calcite to build a single data store for both analytics and transaction processing. It describes some recent improvements to Hive like LLAP (Live Long and Process) that aim to achieve sub-second query response times, as well as using HBase as the Hive metastore to improve performance.
Sub-second-sql-on-hadoop-at-scale
This document discusses strategies for achieving sub-second SQL query performance on Hadoop at scale. It describes two use cases: highly parallel batch reporting on a massive dataset, and online reporting with low latency requirements. For the latter use case, the document evaluates Hive LLAP and Phoenix, finding that Phoenix generally has lower latency, especially for queries with large result sets, through optimizations like skip scans, merging improvements, and table splitting. Tuning HBase and Phoenix configurations can further reduce latency.
Migrating pipelines into Docker
This document discusses Spotify's migration of data pipelines to Docker. It provides background on Spotify growing from 50 to 1000 engineers and the challenges of scaling their big data infrastructure. Spotify adopted Docker to help solve packaging and dependency issues, moving pipelines from cron jobs to a REST API and Docker images. Docker is allowing Spotify to transparently migrate their on-premise Hadoop cluster to Google Cloud, handling over 100 petabytes of data and growing.
What's hot (20)
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Apache Phoenix and HBase: Past, Present and Future of SQL over HBase
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Kafka to Hadoop Ingest with Parsing, Dedup and other Big Data Transformations
Comparative Performance Analysis of AWS EC2 Instance Types Commonly Used for ...
Comparative Performance Analysis of AWS EC2 Instance Types Commonly Used for ...
Hive & HBase for Transaction Processing Hadoop Summit EU Apr 2015
Hive & HBase for Transaction Processing Hadoop Summit EU Apr 2015
Similar to A TPC Benchmark of Hive LLAP and Comparison with Presto
Sql server 2016 it just runs faster sql bits 2017 edition
SQL Server 2016 includes several performance improvements that help it run faster than previous versions:
1. Automatic Soft NUMA partitions workloads across NUMA nodes when there are more than 8 CPUs per node to avoid bottlenecks.
2. Dynamic memory objects are now partitioned by CPU to avoid contention on global memory objects.
3. Redo operations can now be parallelized across multiple tasks to improve performance during database recovery.
Unlock Bigdata Analytic Efficiency with Ceph Data Lake - Zhang Jian, Fu Yong
Cephalocon APAC 2018
March 22-23, 2018 - Beijing, China
Zhang Jian, Intel Software Engineer Manager
Fu Yong, Intel Software Engineer Manager
Hadoop World 2011: Hadoop Network and Compute Architecture Considerations - J...
Cisco's Unified Fabric provides an integrated networking solution optimized for big data infrastructures using Hadoop. The document describes Cisco's testing of the Unified Fabric using a Hadoop cluster of 128 and 16 nodes running Yahoo's Terasort benchmark on 1TB of data. It found that the Unified Fabric can support the network traffic patterns of Hadoop workloads while efficiently utilizing buffering to absorb bursts of traffic during shuffle and replication phases.
SQL Server It Just Runs Faster
Based on the popular blog series, join me in taking a deep dive and a behind the scenes look at how SQL Server 2016 “It Just Runs Faster”, focused on scalability and performance enhancements. This talk will discuss the improvements, not only for awareness, but expose design and internal change details. The beauty behind ‘It Just Runs Faster’ is your ability to just upgrade, in place, and take advantage without lengthy and costly application or infrastructure changes. If you are looking at why SQL Server 2016 makes sense for your business you won’t want to miss this session.
The state of SQL-on-Hadoop in the Cloud
With the increase of Hadoop offerings in the Cloud, users are faced with many decisions to make: which Cloud provider, VMs to choose, cluster sizing, storage type, or even if to go to fully managed Platform-as-a-Service (PaaS) Hadoop? As the answer is always "depends on your data and usage", this talk will guide participants over an overview of the different PaaS solutions for the leading Cloud providers. By highlighting the main results benchmarking their SQL-on-Hadoop (i.e., Hive) services using the ALOJA benchmarking project. To compare their current offerings in terms of readiness, architectural differences, and cost-effectiveness (performance-to-price), to entry-level Hadoop based deployments. As well as briefly presenting how to replicate results and create custom benchmarks from internal apps. So that users can make their own decisions about choosing the right provider to their particular data needs.
The state of SQL-on-Hadoop in the Cloud
The document summarizes the results of a study that evaluated the performance of different Platform-as-a-Service offerings for running SQL on Hadoop workloads. The study tested Amazon EMR, Google Cloud DataProc, Microsoft Azure HDInsight, and Rackspace Cloud Big Data using the TPC-H benchmark at various data sizes up to 1 terabyte. It found that at 1TB, lower-end systems had poorer performance. In general, HDInsight running on D4 instances and Rackspace Cloud Big Data on dedicated hardware had the best scalability and execution times. The study provides insights into the performance, scalability, and price-performance of running SQL on Hadoop in the cloud.
Ceph Performance Profiling and Reporting
Presentation from Ceph Day Sunnyvale 2016 by Brent Compton and Kyle Bader from Red Hat. http://ceph.com/cephdays/
[ACNA2022] Hadoop Vectored IO_ your data just got faster!.pdf
Since 2006 the world of big data has moved from terabytes to hundreds of petabytes, from local clusters to remote cloud storage, yet the original Apache Hadoop POSIX-based file APIs have barely changed.
It is wonderful that these APIs have worked so well, but we can do a lot better with remote object stores, by providing new operations which suit them better, targeted at columnar data libraries such as ORC and Spark. Only a few libraries need to migrate to these APIs for significant speedups of all big data applications.
This talk introduces a new Hadoop Filesystem API called "vectored read", coming in Hadoop 3.4. An extension of the classic FSDataInputStream it is automatically offered by all filesystem clients.
The S3A connector is the first object store to provide a custom implementation, reading different blocks of data in parallel. In Apache Hive benchmarks with a modified ORC library, we saw a 2x speedup compared to using the classic s3a connector through the Posix APIs.
We will introduce the API spec, the S3A implementation, and the benchmarks, and show how to use it in your own applications. We will also cover our ongoing work on providing similar speedups with other object stores, and the use of the API in other applications.
20140120 presto meetup_en
Presto was used to analyze logs collected in a Hadoop cluster. It provided faster query performance compared to Hive+Tez, with results returning in seconds rather than hours. Presto was deployed across worker nodes and performed better than Hive+Tez for different query and data formats. With repeated queries, Presto's performance improved further due to caching, while Hive+Tez showed no change. Overall, Presto demonstrated itself to be a faster solution for interactive queries on large log data.
The state of Hive and Spark in the Cloud (July 2017)
Originally presented at the BDOOP and Spark Barcelona meetup groups: http://meetu.ps/3bwCTM
Cloud providers currently offer convenient on-demand managed big data clusters (PaaS) with a pay-as-you-go model. In PaaS, analytical engines such as Spark and Hive come ready to use, with a general-purpose configuration and upgrade management. Over the last year, the Spark framework and APIs have been evolving very rapidly, with major improvements on performance and the release of v2, making it challenging to keep up-to-date production services both on-premises and in the cloud for compatibility and stability. The talk compares:
• The performance of both v1 and v2 for Spark and Hive
• PaaS cloud services: Azure HDinsight, Amazon Web Services EMR, Google Cloud Dataproc
• Out-of-the-box support for Spark and Hive versions from providers
• PaaS reliability, scalability, and price-performance of the solutions
Using BigBench, the new Big Data benchmark standard. BigBench combines SQL queries, MapReduce, user code (UDF), and machine learning, which makes it ideal to stress Spark libraries (SparkSQL, DataFrames, MLlib, etc.).
Performance Optimizations in Apache Impala
Apache Impala is a modern, open-source MPP SQL engine architected from the ground up for the Hadoop data processing environment. Impala provides low latency and high concurrency for BI/analytic read-mostly queries on Hadoop, not delivered by batch frameworks such as Hive or SPARK. Impala is written from the ground up in C++ and Java. It maintains Hadoop’s flexibility by utilizing standard components (HDFS, HBase, Metastore, Sentry) and is able to read the majority of the widely-used file formats (e.g. Parquet, Avro, RCFile).
To reduce latency, such as that incurred from utilizing MapReduce or by reading data remotely, Impala implements a distributed architecture based on daemon processes that are responsible for all aspects of query execution and that run on the same machines as the rest of the Hadoop infrastructure. Impala employs runtime code generation using LLVM in order to improve execution times and uses static and dynamic partition pruning to significantly reduce the amount of data accessed. The result is performance that is on par or exceeds that of commercial MPP analytic DBMSs, depending on the particular workload. Although initially designed for running on-premises against HDFS-stored data, Impala can also run on public clouds and access data stored in various storage engines such as object stores (e.g. AWS S3), Apache Kudu and HBase. In this talk, we present Impala's architecture in detail and discuss the integration with different storage engines and the cloud.
Super Computer
This document compares the performance of six supercomputers with over 1,000 processors each on various synthetic benchmarks and applications. The supercomputers have different node sizes, processor counts, and interconnect technologies. Performance is analyzed using a model that breaks down run time into computation, communication, and I/O components. Results show that different systems perform best for different benchmarks and applications, depending on factors like the communication requirements and how well the application scales. The Blue Gene supercomputer shows strong scaling and I/O performance but has limitations in processor speed and memory size per node.
Using Derivation-Free Optimization in the Hadoop Cluster with Terasort
We implemented MapReduce cluster benchmark TeraSort by derivative free optimization (DFO) method having runtime function object. In this, every iteration of DFO method uses new values for Hadoop parameter configuration. These parameters are specified within the framework, we used Chef server and client tool which assists in this cluster configuration to ensure proper implementation of TeraSort application.
How Many Slaves (Ukoug)
UKOUG version of a presentation trying to establish the sensible limits of parallelism on a couple of hardware configurations. Detailed white paper is at http://oracledoug.com/px_slaves.pdf
PostgreSQL 10: What to Look For
PostgreSQL 10 will include several new features and improvements, including logical replication which allows replicating specific tables, quorum-based synchronous replication, improved partitioning support, pushing more computations to foreign databases with FDWs, expanded parallel query capabilities, techniques to reduce write amplification, indirect indexes, executor and statistics overhauls, and easier backup/replication configuration defaults. Many of these features are still in development.
Hadoop Hardware @Twitter: Size does matter!
At Twitter we started out with a large monolithic cluster that served most of the use-cases. As the usage expanded and the cluster grew accordingly, we realized we needed to split the cluster by access pattern. This allows us to tune the access policy, SLA, and configuration for each cluster. We will explain our various use-cases, their performance requirements, and operational considerations and how those are served by the corresponding clusters. We will discuss what our baseline Hadoop node looks like. Various, sometimes competing, considerations such as storage size, disk IO, CPU throughput, fewer fast cores versus many slower cores, 1GE bonded network interfaces versus a single 10 GE card, 1T, 2T or 3T disk drives, and power draw all need to be considered in a trade-off where cost and performance are major factors. We will show how we have arrived at quite different hardware platforms at Twitter, not only saving money, but also increasing performance.
Deep Learning with Apache Spark and GPUs with Pierce Spitler
Apache Spark is a powerful, scalable real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters are fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they will need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage.
This session will cover:
– How to leverage Spark and TensorFlow for hyperparameter tuning and for deploying trained models
– DeepLearning4J, CaffeOnSpark, IBM’s SystemML and Intel’s BigDL
– Sidecar GPU cluster architecture and Spark-GPU data reading patterns
– The pros, cons and performance characteristics of various approaches
You’ll leave the session better informed about the available architectures for Spark and deep learning, and Spark with and without GPUs for deep learning. You’ll also learn about the pros and cons of deep learning software frameworks for various use cases, and discover a practical, applied methodology and technical examples for tackling big data deep learning.
Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction wi...
In this talk, we will present how we analyze, predict, and visualize network quality data, as a spark AI use case in a telecommunications company. SK Telecom is the largest wireless telecommunications provider in South Korea with 300,000 cells and 27 million subscribers. These 300,000 cells generate data every 10 seconds, the total size of which is 60TB, 120 billion records per day.
In order to address previous problems of Spark based on HDFS, we have developed a new data store for SparkSQL consisting of Redis and RocksDB that allows us to distribute and store these data in real time and analyze it right away, We were not satisfied with being able to analyze network quality in real-time, we tried to predict network quality in near future in order to quickly detect and recover network device failures, by designing network signal pattern-aware DNN model and a new in-memory data pipeline from spark to tensorflow.
In addition, by integrating Apache Livy and MapboxGL to SparkSQL and our new store, we have built a geospatial visualization system that shows the current population and signal strength of 300,000 cells on the map in real time.
Linux Kernel vs DPDK: HTTP Performance Showdown
In this session I will use a simple HTTP benchmark to compare the performance of the Linux kernel networking stack with userspace networking powered by DPDK (kernel-bypass).
It is said that kernel-bypass technologies avoid the kernel because it is "slow", but in reality, a lot of the performance advantages that they bring just come from enforcing certain constraints.
As it turns out, many of these constraints can be enforced without bypassing the kernel. If the system is tuned just right, one can achieve performance that approaches kernel-bypass speeds, while still benefiting from the kernel's battle-tested compatibility, and rich ecosystem of tools.
WMS Performance Shootout 2009
This document summarizes the results of a benchmark test comparing the performance of GeoServer and MapServer web map server (WMS) implementations against different data backends and workloads. Key findings include that GeoServer was generally faster than MapServer at reading shapefiles and rendering plain polygons. Performance was similar between the two when using PostGIS and Oracle spatial backends. MapServer showed improved performance for labelled roads rendering compared to previous tests. Areas for potential improvement in future tests are also discussed.
Similar to A TPC Benchmark of Hive LLAP and Comparison with Presto (20)
Sql server 2016 it just runs faster sql bits 2017 edition
Sql server 2016 it just runs faster sql bits 2017 edition
Unlock Bigdata Analytic Efficiency with Ceph Data Lake - Zhang Jian, Fu Yong
Unlock Bigdata Analytic Efficiency with Ceph Data Lake - Zhang Jian, Fu Yong
Hadoop World 2011: Hadoop Network and Compute Architecture Considerations - J...
Hadoop World 2011: Hadoop Network and Compute Architecture Considerations - J...
[ACNA2022] Hadoop Vectored IO_ your data just got faster!.pdf
[ACNA2022] Hadoop Vectored IO_ your data just got faster!.pdf
The state of Hive and Spark in the Cloud (July 2017)
The state of Hive and Spark in the Cloud (July 2017)
Using Derivation-Free Optimization in the Hadoop Cluster with Terasort
Using Derivation-Free Optimization in the Hadoop Cluster with Terasort
Deep Learning with Apache Spark and GPUs with Pierce Spitler
Deep Learning with Apache Spark and GPUs with Pierce Spitler
Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction wi...
Apache Spark AI Use Case in Telco: Network Quality Analysis and Prediction wi...
More from Yu Liu
Cloud Era Transactional Processing -- Problems, Strategies and Solutions
The document discusses challenges and solutions for transactional processing in the cloud era. It covers modeling transactional consistency constraints, choosing appropriate consistency models like causal consistency, and state-of-the-art academic research in coordination avoidance, consistency models, and hardware efforts to improve transaction processing performance. The document provides definitions of consistency models and isolation levels and compares different approaches.
Introduction to NTCIR 2016 MedNLPDoc
The document discusses natural language processing (NLP) for medical documents, specifically retrieving International Classification of Diseases (ICD) codes from free-text medical reports. It summarizes a medical NLP shared task called MedNLPDoc that aimed to retrieve information from Japanese medical reports. The highest performing system used a rule-based approach, showing rules can still outperform machine learning for medical NLP. Collaboration between researchers and enterprises was encouraged to resolve gaps between academic research and real-world requirements.
高性能データ処理プラットフォーム (Talk on July Tech Festa 2015)
Introduction to a Scalable, High-Performance Distributed Data Processing Platform (WorksApplications)
http://2016.techfesta.jp/
Survey on Parallel/Distributed Search Engines
This document summarizes a survey on parallel and distributed search engines. It discusses how web search tasks like crawling billions of documents, indexing terabytes of data, and responding to thousands of queries simultaneously require a parallel or distributed approach. It then provides examples of distributed search engines and technologies like MapReduce, and discusses challenges in distributed search like resource representation, selection, and result merging. Finally, it surveys parallel implementations of clustering algorithms and challenges in parallelizing hierarchical agglomerative clustering with MapReduce.
Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth
This slides introduced the paper: H. L. Bodlaender and a. M. C. a. Koster, “Combinatorial Optimization on Graphs of Bounded Treewidth,” Comput. J., vol. 51, no. 3, pp. 255–269, Nov. 2007.
Paper Introduction: Combinatorial Model and Bounds for Target Set Selection
The paper Combinatorial Model and Bounds for Target Set Selection by Eyal Ackerman, Oren Ben-Zwi, Guy Wolfovitz:
1. a combinatorial model for the dynamic activation process of
influential networks;
2. representing Perfect Target Set Selection Problem and its
variants by linear integer programs;
3. combinatorial lower and upper bounds on the size of the
minimum Perfect Target Set
An accumulative computation framework on MapReduce ppl2013
The document discusses an accumulative computation framework on MapReduce clusters. It presents examples of accumulative computation programs and benchmarks their performance on MapReduce. The experiments show the framework can process large datasets in a reasonable time and achieves near-linear speedup when increasing CPUs, demonstrating the efficiency and scalability of the approach. The accumulative computation pattern and framework simplify parallelizing problems that have data dependencies and allow encoding many parallel computations.
An Enhanced MapReduce Model (on BSP)
Using Apache Hama to implement a new MapReduce Programming interface and obtaining 10~20 times speedup in general.
A Homomorphism-based Framework for Systematic Parallel Programming with MapRe...
This document describes a homomorphism-based framework for systematic parallel programming with MapReduce. The framework introduces a systematic approach to automatically generate fully parallelized and scalable MapReduce programs. It provides algorithmic programming interfaces that allow users to focus on the algebraic properties of problems, hiding the details of MapReduce. The framework was implemented on top of Hadoop and evaluated on several test problems, demonstrating good scalability and parallelism. Future work could decrease system overhead, optimize performance further, and extend the framework to more complex data structures like trees and graphs.
An Introduction of Recent Research on MapReduce (2011)
This document summarizes recent research on MapReduce. It outlines papers presented at the MAPREDUCE11 conference and Hadoop World 2010, including papers on resource attribution in data clusters, shared-memory MapReduce implementations, static type checking of MapReduce programs, QR factorizations, genome indexing, and optimizing data selection. It also summarizes talks and lists several interesting papers on topics like distributed data processing.
A Generate-Test-Aggregate Parallel Programming Library on Spark
Generate-Test-Aggregate is a library for systematic parallel programming of MapReduce. This slides is made for my presentation on PPoPP - PMAM 2013.
Introduction of A Lightweight Stage-Programming Framework
The Lightweight Stage-Programming Framework introduced in this slides can be used for making efficient parallel DSL which can be transformed to MapReduce programs. To understand this slides, please firstly read http://www.slideshare.net/YuLiu19/a-generatetestaggregate-parallel-programming-library-on-spark.
Start From A MapReduce Graph Pattern-recognize Algorithm
This document summarizes a presentation on developing a MapReduce algorithm to recognize patterns in large graphs by finding connected components. It discusses:
- Motivation to study parallel graph algorithms and frameworks like MapReduce and Pregel
- The problem of finding link patterns in graphs by extracting connected components
- Background on semantic web and linked open data modeled as RDF graphs
- A naive O(2Ck)-iteration MapReduce algorithm to find connected components between pairs of datasets
- Examples and analysis of the algorithm's complexity and communication costs
Introduction of the Design of A High-level Language over MapReduce -- The Pig...
Pig is a platform for analyzing large datasets that uses Pig Latin, a high-level language, to express data analysis programs. Pig Latin programs are compiled into MapReduce jobs and executed on Hadoop. Pig Latin provides data manipulation constructs like SQL as well as user-defined functions. The Pig system compiles programs through optimization, code generation, and execution on Hadoop. Future work focuses on additional optimizations, non-Java UDFs, and interfaces like SQL.
On Extending MapReduce - Survey and Experiments
It talks a survey and my experiments on extending MapReduce programming model. A BSP-based MapReduce interface was implemented and evaluated, which shows dramatically improvement on performance.
Tree representation in map reduce world
It discussed how to represent a tree-like data structure in MapReduce world and applied on applications like Hieratical Clustering.
Introduction to Ultra-succinct representation of ordered trees with applications
The document summarizes a paper on ultra-succinct representations of ordered trees. It introduces tree degree entropy, a new measure of information in trees. It presents a succinct data structure that uses nH*(T) + O(n log log n / log n) bits to represent an ordered tree T with n nodes, where H*(T) is the tree degree entropy. This representation supports computing consecutive bits of the tree's DFUDS representation in constant time. It also supports computing operations like lowest common ancestor, depth, and level-ancestor in constant time using an auxiliary structure of O(n(log log n)2 / log n) bits.
On Implementation of Neuron Network(Back-propagation)
This document outlines Yu Liu's work implementing and comparing different parallel versions of a neural network using backpropagation. It discusses motivations for parallel programming practice and library study. It provides an introduction to neural networks and backpropagation algorithms. Three implementations are compared: sequential C++ STL, Skelton library, and Intel TBB. Benchmark results show improved speedups from parallel versions. Remaining challenges are also noted, like addressing local minima problems and testing on larger data.
ScrewDriver Rebirth: Generate-Test-and-Aggregate Framework on Hadoop
This document describes Yu Liu's ScrewDriver Rebirth framework for implementing the generate-test-aggregate algorithm on Hadoop. The framework uses semiring structures to represent the generate, test, and aggregate functions. It defines Generator and Aggregater classes to implement generation and aggregation. The framework allows fusing operations by lifting semirings and defining new generators. Examples show various generators, tests, and aggregators run on Hadoop to evaluate performance improvements over the previous version.
A Homomorphism-based MapReduce Framework for Systematic Parallel Programming
The document outlines a homomorphism-based framework for parallel programming on MapReduce. It introduces homomorphisms and theorems about them. The framework represents lists as sets of key-value pairs distributed across nodes. Functions are implemented using this representation and MapReduce, allowing easy parallelization of problems like maximum prefix sum that are otherwise complex on MapReduce.
More from Yu Liu (20)
Cloud Era Transactional Processing -- Problems, Strategies and Solutions
Cloud Era Transactional Processing -- Problems, Strategies and Solutions
Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth
Paper introduction to Combinatorial Optimization on Graphs of Bounded Treewidth
Paper Introduction: Combinatorial Model and Bounds for Target Set Selection
Paper Introduction: Combinatorial Model and Bounds for Target Set Selection
An accumulative computation framework on MapReduce ppl2013
An accumulative computation framework on MapReduce ppl2013
A Homomorphism-based Framework for Systematic Parallel Programming with MapRe...
A Homomorphism-based Framework for Systematic Parallel Programming with MapRe...
An Introduction of Recent Research on MapReduce (2011)
An Introduction of Recent Research on MapReduce (2011)
A Generate-Test-Aggregate Parallel Programming Library on Spark
A Generate-Test-Aggregate Parallel Programming Library on Spark
Introduction of A Lightweight Stage-Programming Framework
Introduction of A Lightweight Stage-Programming Framework
Start From A MapReduce Graph Pattern-recognize Algorithm
Start From A MapReduce Graph Pattern-recognize Algorithm
Introduction of the Design of A High-level Language over MapReduce -- The Pig...
Introduction of the Design of A High-level Language over MapReduce -- The Pig...
Introduction to Ultra-succinct representation of ordered trees with applications
Introduction to Ultra-succinct representation of ordered trees with applications
On Implementation of Neuron Network(Back-propagation)
On Implementation of Neuron Network(Back-propagation)
ScrewDriver Rebirth: Generate-Test-and-Aggregate Framework on Hadoop
ScrewDriver Rebirth: Generate-Test-and-Aggregate Framework on Hadoop
A Homomorphism-based MapReduce Framework for Systematic Parallel Programming
A Homomorphism-based MapReduce Framework for Systematic Parallel Programming
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6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
CBU毕业证offer【微信95270640】《卡普顿大学毕业证书》《QQ微信95270640》学位证书电子版：在线制作卡普顿大学毕业证成绩单GPA修改（制作CBU毕业证成绩单CBU文凭证书样本）、卡普顿大学毕业证书与成绩单样本图片、《CBU学历证书学位证书》、卡普顿大学毕业证案例毕业证书制作軟體、在线制作加拿大硕士学历证书真实可查.
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡普顿大学卡普顿大学毕业证成绩单；
3、不清楚流程以及材料该如何准备卡普顿大学卡普顿大学毕业证成绩单；
4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★卡普顿大学卡普顿大学毕业证成绩单毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
【★真实留信认证，留信网入库存档，可查卡普顿大学卡普顿大学毕业证成绩单】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡普顿大学同学朋友
你做代理，可以拯救卡普顿大学失足青年
你做代理，可以挽救卡普顿大学一个个人才
你做代理，你将是别人人生卡普顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会道银边山娃摸索着扯了扯灯绳小屋顿时一片刺眼的亮瞅瞅床头的诺基亚山娃苦笑着摇了摇头连他自己都感到奇怪居然又睡到上午点半掐指算算随父亲进城已一个多星期了山娃几乎天天起得这么迟在乡下老家暑假五点多山娃就醒来在爷爷奶奶嘁嘁喳喳的忙碌声中一骨碌爬起把牛驱到后龙山再从莲塘里采回一蛇皮袋湿漉漉的莲蓬也才点多点半早就吃过早餐玩耍去了山娃的家在闽西山区依山傍水山清水秀门前潺潺流淌的蜿蜒小溪一直都是山娃和小伙伴们盛试
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Analysis insight about a Flyball dog competition team's performance
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
State of Artificial intelligence Report 2023
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
一比一原版(Dalhousie毕业证书)达尔豪斯大学毕业证如何办理
原版定制【微信:41543339】【(Dalhousie毕业证书)达尔豪斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(Coventry毕业证书)考文垂大学毕业证如何办理
毕业原版【微信:41543339】【(Coventry毕业证书)考文垂大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
University of New South Wales degree offer diploma Transcript
澳洲UNSW毕业证书制作新南威尔士大学假文凭定制Q微168899991做UNSW留信网教留服认证海牙认证改UNSW成绩单GPA做UNSW假学位证假文凭高仿毕业证申请新南威尔士大学University of New South Wales degree offer diploma Transcript
Natural Language Processing (NLP), RAG and its applications .pptx
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
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4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
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Natural Language Processing (NLP), RAG and its applications .pptx
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A TPC Benchmark of Hive LLAP and Comparison with Presto
- 1. A Benchmark & Comparison on Hive (2.1.0) LLAP and Presto (0.208) Data Engineering@SmartNews Yu Liu 2018/10/01
- 2. Background and Preliminaries TPC-DS and TPC-H benchmark suits Hive LLAP (link) Similar results from Hortonworks and Microsoft
- 3. Conclusions for Impatience ❏ Hive LLAP brings significant improvements on performance ❏ Hive LLAP is outperformed on TPC-DS, compared with non-LLAP and Presto ❏ Presto shows some advantages on TPC-H ❏ Hive LLAP causes bigger footprints of RAM usage and need careful tuning
- 4. Environment Setting Cluster configurations (AWS EC2): ❏ 1 X Master : r4.xlarge (4 vCPU - 32GB RAM) ❏ 10 X Workers : i3.large (2 vCPU - 16GB RAM) Stacks: ❏ HDP 2.6 (Hive 2.1.0, Tez 0.7.0, Calcite 1.2.0) ❏ Presto 0.208 TPC Data Sets ❏ 10 GB Text/ORC format for both DS and H
- 5. Results and Comparisons The way of calculating query performance in this table including “query-duration” and “job-submission” time.
- 6. Dive into Details In total 99 queries Hive LLAP Presto Comparison (H v P) Faster cases (num) 53 17 3.1 times Total Runtime (s) 1351 2058 65.6% (1.5 times faster) Failed cases (num) 0 21 (OOM or syntax error) Due to the time constraint, the benchmark used a same set of SQLs for Hive 2
- 7. Biggest Differences Query No. Presto (sec.) Hive LLAP (sec.) Difference ratio query39 188 29 6.48 query47 56 13 4.31 query9 28 7 4 query44 8 30 3.75 query22 48 161 3.35 query88 50 15 3.33
- 8. Compare with Current EMR(5) Installation Conditions: ➔ Input Data: same (24 tables,10 GB text and converted to ORC format) ➔ TPC-DS queries on ORC tables ➔ EMR 5 instances spec. are much better (worker nodes = 16 vCPU, 32GB RAM x 10) Results: tens of times faster than EMR5 (details in next slide) Reasons: 1. LLAP setting 2. ORC file format issue 3. Other Hive configurations
- 9. TPC-DS Queries Duration Time Query No. Run on EMR(5) in sec. Run on Hive LLAP in sec. Difference (times) No. 1 130.717 1.855 70 No. 11 184.94 7.434 25 No. 12 31.565 1.384 23 No. 50 90.435 11.576 8 No. 66 140.1 3.742 38 The way of calculating query performance in this table only including “query-duration” time.
- 10. Many other details are omitted The following details are omitted because of space, in general and obviously they are slower ❏ Hive with LLAP on MR engine ❏ Hive with Tez without LLAP
- 11. Future work ❏ Hive LLAP with Druid as the storage engine ❏ Compare with EMR 5.0 installation (with some constraint on supporting ORC) ❏ Parquet vs ORC ❏ ORC with different compaction strategies (BI/ETL/Hybrid) ❏ Evaluation on creations of bloom filter and zone map